Imaging biomarkers are features in images that have biological implications. For example, in a picture of a person with red hair, the red hair is a feature and the implication is that there is a mutation in the MC1R gene that provides instructions for making a protein called the melanocortin 1 receptor. This feature, an imaging biomarker, can be used as a medical cue to indicate increased risk for melanoma. When used in this context, this imaging biomarker becomes an imaging biomarker cue (IBC), in the sense that it may cue the medical professional observer to alter treatment accordingly, such as recommending sunscreen use. IBCs do not individually bear the full weight of medical decision-making and instead are integrated. IBC analysis may be a process of sensory cue integration or may be a process of observation and integration by technology such as a digital camera and computer. An advantage of the latter is that computational scalability enables machine vision to compute vast permutations of IBCs that would be overwhelming to a human observer. Thus computers can try many potential diagnostic methods rapidly before picking the best one to teach back to humans. The purpose of this project is to develop a human/machine interface for bi-directional teaching so expert dermatologists can teach computers what IBCs they use to achieve accurate diagnosis and computers can teach dermatologists the best way to use current IBCs and suggest integration of new IBCs that machine learning guides them to. As an outcome, we will measure the diagnostic performance of dermatologists who undergo IBC training in detecting melanoma. It is known that early detection saves lives, but the potential of technology to improve early detection, a great need since 10,000 Americans still die each year from melanoma, is unknown. This project will help answer that unknown and if we are successful in translating IBCs with commuter vision and machine learning, more melanomas will be detected early and lives will be saved. Our long-term goal is to reduce melanoma related deaths and unnecessary biopsies by helping clinicians increase the predictive value of dermoscopy-based melanoma screening. We believe sensitivity and specificity of dermoscopy- based melanoma screening for non-expert screeners can be improved by assistive technology, which is highly desirable given the cost of false positives (patient stress and unnecessary biopsies) and the extremely high cost of false negatives (delayed melanoma treatment).